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---
language:
- fr
license: apache-2.0
tags:
- automatic-speech-recognition
- mozilla-foundation/common_voice_9_0
- generated_from_trainer
- hf-asr-leaderboard
- robust-speech-event
datasets:
- mozilla-foundation/common_voice_9_0
model-index:
- name: Fine-tuned Wav2Vec2 XLS-R 1B model for ASR in French
results:
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: Common Voice 9
type: mozilla-foundation/common_voice_9_0
args: fr
metrics:
- name: Test WER
type: wer
value: 12.72
- name: Test CER
type: cer
value: 3.78
- name: Test WER (+LM)
type: wer
value: 10.60
- name: Test CER (+LM)
type: cer
value: 3.41
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: Robust Speech Event - Dev Data
type: speech-recognition-community-v2/dev_data
args: fr
metrics:
- name: Test WER
type: wer
value: 24.28
- name: Test CER
type: cer
value: 11.46
- name: Test WER (+LM)
type: wer
value: 20.85
- name: Test CER (+LM)
type: cer
value: 11.09
---
# Fine-tuned Wav2Vec2 XLS-R 1B model for ASR in French
This model is a fine-tuned version of [facebook/wav2vec2-xls-r-1b](https://huggingface.co/facebook/wav2vec2-xls-r-1b) on the MOZILLA-FOUNDATION/COMMON_VOICE_9_0 - FR dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1430
- Wer: 0.1245
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0001
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 8
- total_train_batch_size: 128
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 10.0
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:-----:|:---------------:|:------:|
| 0.9229 | 0.14 | 500 | 0.5049 | 0.4008 |
| 0.3823 | 0.28 | 1000 | 0.2831 | 0.2297 |
| 0.3079 | 0.42 | 1500 | 0.2385 | 0.1951 |
| 0.2899 | 0.55 | 2000 | 0.2273 | 0.1978 |
| 0.2795 | 0.69 | 2500 | 0.2329 | 0.1983 |
| 0.2863 | 0.83 | 3000 | 0.2289 | 0.1991 |
| 0.3063 | 0.97 | 3500 | 0.2370 | 0.2046 |
| 0.2766 | 1.11 | 4000 | 0.2322 | 0.2021 |
| 0.2749 | 1.25 | 4500 | 0.2332 | 0.2055 |
| 0.2769 | 1.39 | 5000 | 0.2322 | 0.2035 |
| 0.2628 | 1.53 | 5500 | 0.2242 | 0.1948 |
| 0.2614 | 1.66 | 6000 | 0.2303 | 0.1962 |
| 0.2547 | 1.8 | 6500 | 0.2238 | 0.1920 |
| 0.2458 | 1.94 | 7000 | 0.2186 | 0.1894 |
| 0.231 | 2.08 | 7500 | 0.2169 | 0.1895 |
| 0.2309 | 2.22 | 8000 | 0.2131 | 0.1870 |
| 0.2258 | 2.36 | 8500 | 0.2133 | 0.1818 |
| 0.2278 | 2.5 | 9000 | 0.2176 | 0.1878 |
| 0.2263 | 2.63 | 9500 | 0.2030 | 0.1813 |
| 0.2262 | 2.77 | 10000 | 0.2077 | 0.1824 |
| 0.2228 | 2.91 | 10500 | 0.2115 | 0.1840 |
| 0.2118 | 3.05 | 11000 | 0.2093 | 0.1782 |
| 0.2073 | 3.19 | 11500 | 0.2004 | 0.1756 |
| 0.2015 | 3.33 | 12000 | 0.1988 | 0.1748 |
| 0.214 | 3.47 | 12500 | 0.2088 | 0.1816 |
| 0.2075 | 3.61 | 13000 | 0.1976 | 0.1746 |
| 0.2039 | 3.74 | 13500 | 0.1958 | 0.1744 |
| 0.2003 | 3.88 | 14000 | 0.1931 | 0.1693 |
| 0.1886 | 4.02 | 14500 | 0.1964 | 0.1686 |
| 0.1943 | 4.16 | 15000 | 0.1986 | 0.1746 |
| 0.1919 | 4.3 | 15500 | 0.1957 | 0.1700 |
| 0.1857 | 4.44 | 16000 | 0.1907 | 0.1671 |
| 0.1834 | 4.58 | 16500 | 0.1877 | 0.1641 |
| 0.18 | 4.71 | 17000 | 0.1828 | 0.1600 |
| 0.1774 | 4.85 | 17500 | 0.1863 | 0.1605 |
| 0.1755 | 4.99 | 18000 | 0.1833 | 0.1595 |
| 0.1692 | 5.13 | 18500 | 0.1814 | 0.1569 |
| 0.1674 | 5.27 | 19000 | 0.1819 | 0.1566 |
| 0.1664 | 5.41 | 19500 | 0.1805 | 0.1572 |
| 0.1677 | 5.55 | 20000 | 0.1803 | 0.1560 |
| 0.1637 | 5.68 | 20500 | 0.1750 | 0.1525 |
| 0.1628 | 5.82 | 21000 | 0.1774 | 0.1532 |
| 0.1645 | 5.96 | 21500 | 0.1744 | 0.1527 |
| 0.1551 | 6.1 | 22000 | 0.1778 | 0.1543 |
| 0.1505 | 6.24 | 22500 | 0.1754 | 0.1528 |
| 0.1499 | 6.38 | 23000 | 0.1743 | 0.1500 |
| 0.1491 | 6.52 | 23500 | 0.1684 | 0.1473 |
| 0.1477 | 6.66 | 24000 | 0.1661 | 0.1472 |
| 0.1456 | 6.79 | 24500 | 0.1654 | 0.1440 |
| 0.1415 | 6.93 | 25000 | 0.1654 | 0.1448 |
| 0.136 | 7.07 | 25500 | 0.1616 | 0.1407 |
| 0.132 | 7.21 | 26000 | 0.1625 | 0.1410 |
| 0.1323 | 7.35 | 26500 | 0.1604 | 0.1404 |
| 0.1338 | 7.49 | 27000 | 0.1574 | 0.1386 |
| 0.13 | 7.63 | 27500 | 0.1576 | 0.1384 |
| 0.1291 | 7.76 | 28000 | 0.1551 | 0.1366 |
| 0.1277 | 7.9 | 28500 | 0.1542 | 0.1356 |
| 0.1241 | 8.04 | 29000 | 0.1545 | 0.1350 |
| 0.1198 | 8.18 | 29500 | 0.1536 | 0.1322 |
| 0.1204 | 8.32 | 30000 | 0.1547 | 0.1337 |
| 0.1195 | 8.46 | 30500 | 0.1494 | 0.1309 |
| 0.1169 | 8.6 | 31000 | 0.1490 | 0.1300 |
| 0.1159 | 8.74 | 31500 | 0.1485 | 0.1305 |
| 0.1142 | 8.87 | 32000 | 0.1479 | 0.1292 |
| 0.1087 | 9.01 | 32500 | 0.1471 | 0.1284 |
| 0.1076 | 9.15 | 33000 | 0.1467 | 0.1270 |
| 0.1078 | 9.29 | 33500 | 0.1467 | 0.1270 |
| 0.1073 | 9.43 | 34000 | 0.1447 | 0.1256 |
| 0.108 | 9.57 | 34500 | 0.1447 | 0.1257 |
| 0.106 | 9.71 | 35000 | 0.1438 | 0.1255 |
| 0.1052 | 9.84 | 35500 | 0.1428 | 0.1247 |
| 0.1044 | 9.98 | 36000 | 0.1430 | 0.1245 |
## Evaluation
1. To evaluate on `mozilla-foundation/common_voice_9_0`
```bash
python eval.py \
--model_id "bhuang/wav2vec2-xls-r-1b-french" \
--dataset "mozilla-foundation/common_voice_9_0" \
--config "fr" \
--split "test" \
--log_outputs
```
2. To evaluate on `speech-recognition-community-v2/dev_data`
```bash
python eval.py \
--model_id "bhuang/wav2vec2-xls-r-1b-french" \
--dataset "speech-recognition-community-v2/dev_data" \
--config "fr" \
--split "validation" \
--chunk_length_s 5.0 \
--stride_length_s 1.0 \
--log_outputs
```
### Framework versions
- Transformers 4.22.0.dev0
- Pytorch 1.12.0+cu113
- Datasets 2.4.0
- Tokenizers 0.12.1
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